The present application relates to optical mark recognition (OMR). It finds particular application in connection with a method and apparatus for automatic recognition of when a hand-drawn mark has been made within a particular region of a user-completed document, such as a standardized test form.
Machine readable forms are now widely used for a variety of applications, including for standardized tests, job applications, survey questionnaires, census data collection, inventory control, market research, and the like. Such forms have preprinted data as well as one or more designated fields for user input. Typically, a person completing the form is asked to make handwritten marks, such as check marks, completed circles, or a signature, in pencil or ink in designated fields of the preprinted form in order to designate answers to various queries or sign the form. The form containing the user's handwritten marks is then later processed to identify the user's responses. This may entail scanning the form with an optical scanner. The designated fields are then identified, e.g., by comparing the scanned form to a blank form and compensating for any change in orientation and/or size of the form during the scanning process. The designated fields are then automatically examined and user-applied marks are recognized. Based on the marks recognized, responses of the user are inferred. If the form is a test form, the responses may be compared with predetermined responses for scoring purposes.
Optical mark recognition refers to the process of recognizing the presence of handwritten marks on a scanned document. OMR is somewhat different from optical character recognition (OCR), which seeks to assign a single character from a predefined character set to each printed mark. OMR aims to identify whether a user has applied a mark to a designated field. While there are many techniques for performing optical mark recognition, the techniques are typically very sensitive to both the original mark quality as well as document scanning factors (resolution, image quality, and registration). In the case of registration, this can only be achieved to within a few pixels, e.g., up to a dozen pixels. A misregistration of 5-10 pixels can, however, have measurable effects on the ability to identify marks within small checkboxes. Human markings also exhibit considerable variability. Users have their own ways of checking a checkbox, for example, which can have significant effects on the recognition. Image quality and scan variability also impact recognition. In particular, for grayscale forms, thresholding variations can change a given document considerably, such that simple black pixel counts do not always work well for identifying marks.